IDEAS home Printed from https://ideas.repec.org/a/eee/inteco/v157y2019icp23-32.html
   My bibliography  Save this article

Volatility estimation for Bitcoin: Replication and robustness*

* This paper is a replication of an original study

Author

Listed:
  • Charles, Amélie
  • Darné, Olivier

Abstract

Katsiampa [Volatility estimation for Bitcoin: A comparison of GARCH models. Economics Letters, 158, 3–6, 2017] compares several GARCH-type models to estimate volatility for Bitcoin returns. First, we propose a replication study (i) by verification, using the same sample and period (July 2010 to October 2016), and (ii) by reproduction, extending the sample until March 2018. We obtain only partially different results from those of Kasiampa (2017) on both samples. Second, we propose a robustness analysis (i) by reanalysis, using the robust QML estimator for computing the standard errors of the parameters, and (ii) by extension, taking into account the presence of jumps in the Bitcoin returns. The results show that the six GARCH-type models studied, namely GARCH-type models characterized by short memory, asymmetric effects, or long-run and short-run movements, seem not to be appropriate for modelling the Bitcoin returns.

Suggested Citation

  • Charles, Amélie & Darné, Olivier, 2019. "Volatility estimation for Bitcoin: Replication and robustness," International Economics, Elsevier, vol. 157(C), pages 23-32.
    • Handle: RePEc:eee:inteco:v:157:y:2019:i:c:p:23-32
    DOI: 10.1016/j.inteco.2018.06.004
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S2110701718300088
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.inteco.2018.06.004?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to look for a different version below or search for a different version of it.

    Other versions of this item:

    References listed on IDEAS

    as
    1. Laurent, Sébastien & Lecourt, Christelle & Palm, Franz C., 2016. "Testing for jumps in conditionally Gaussian ARMA–GARCH models, a robust approach," Computational Statistics & Data Analysis, Elsevier, vol. 100(C), pages 383-400.
    2. Michael A. Clemens, 2017. "The Meaning Of Failed Replications: A Review And Proposal," Journal of Economic Surveys, Wiley Blackwell, vol. 31(1), pages 326-342, February.
    3. Ciaian, Pavel & Rajcaniova, Miroslava & Kancs, d'Artis, 2018. "Virtual relationships: Short- and long-run evidence from BitCoin and altcoin markets," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 52(C), pages 173-195.
    4. M. Angeles Carnero & Daniel Peña & Esther Ruiz, 2007. "Effects of outliers on the identification and estimation of GARCH models," Journal of Time Series Analysis, Wiley Blackwell, vol. 28(4), pages 471-497, July.
    5. Glosten, Lawrence R & Jagannathan, Ravi & Runkle, David E, 1993. "On the Relation between the Expected Value and the Volatility of the Nominal Excess Return on Stocks," Journal of Finance, American Finance Association, vol. 48(5), pages 1779-1801, December.
    6. Daniel S. Hamermesh, 2007. "Replication in Economics," NBER Working Papers 13026, National Bureau of Economic Research, Inc.
    7. Bollerslev, Tim, 1986. "Generalized autoregressive conditional heteroskedasticity," Journal of Econometrics, Elsevier, vol. 31(3), pages 307-327, April.
    8. Bouri, Elie & Azzi, Georges & Dyhrberg, Anne Haubo, 2017. "On the return-volatility relationship in the Bitcoin market around the price crash of 2013," Economics - The Open-Access, Open-Assessment E-Journal (2007-2020), Kiel Institute for the World Economy (IfW Kiel), vol. 11, pages 1-16.
    9. Rabemananjara, R & Zakoian, J M, 1993. "Threshold Arch Models and Asymmetries in Volatility," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 8(1), pages 31-49, Jan.-Marc.
    10. Christophe Hurlin & Christophe Pérignon & Victoria Stodden, 2012. "RunMyCode.org: a novel dissemination and collaboration platform for executing published computational results," Working Papers halshs-00739233, HAL.
    11. Andersen, Torben G. & Bollerslev, Tim & Dobrev, Dobrislav, 2007. "No-arbitrage semi-martingale restrictions for continuous-time volatility models subject to leverage effects, jumps and i.i.d. noise: Theory and testable distributional implications," Journal of Econometrics, Elsevier, vol. 138(1), pages 125-180, May.
    12. Suzanne S. Lee & Per A. Mykland, 2008. "Jumps in Financial Markets: A New Nonparametric Test and Jump Dynamics," Review of Financial Studies, Society for Financial Studies, vol. 21(6), pages 2535-2563, November.
    13. Roger Koenker & Achim Zeileis, 2009. "On reproducible econometric research," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 24(5), pages 833-847.
    14. Boudt, Kris & Daníelsson, Jón & Laurent, Sébastien, 2013. "Robust forecasting of dynamic conditional correlation GARCH models," International Journal of Forecasting, Elsevier, vol. 29(2), pages 244-257.
    15. Nelson, Daniel B, 1991. "Conditional Heteroskedasticity in Asset Returns: A New Approach," Econometrica, Econometric Society, vol. 59(2), pages 347-370, March.
    16. Ding, Zhuanxin & Granger, Clive W. J. & Engle, Robert F., 1993. "A long memory property of stock market returns and a new model," Journal of Empirical Finance, Elsevier, vol. 1(1), pages 83-106, June.
    17. Daniel S. Hamermesh, 2007. "Viewpoint: Replication in economics," Canadian Journal of Economics, Canadian Economics Association, vol. 40(3), pages 715-733, August.
    18. Arulampalam, Wiji & Hartog, Joop & MaCurdy, Tom & Theeuwes, Jules, 1997. "Replication and re-analysis," Labour Economics, Elsevier, vol. 4(2), pages 99-105, June.
    19. Baur, Dirk G. & Dimpfl, Thomas & Kuck, Konstantin, 2018. "Bitcoin, gold and the US dollar – A replication and extension," Finance Research Letters, Elsevier, vol. 25(C), pages 103-110.
    20. Katsiampa, Paraskevi, 2017. "Volatility estimation for Bitcoin: A comparison of GARCH models," Economics Letters, Elsevier, vol. 158(C), pages 3-6.
    21. Dyhrberg, Anne Haubo, 2016. "Bitcoin, gold and the dollar – A GARCH volatility analysis," Finance Research Letters, Elsevier, vol. 16(C), pages 85-92.
    22. Dwyer, Gerald P., 2015. "The economics of Bitcoin and similar private digital currencies," Journal of Financial Stability, Elsevier, vol. 17(C), pages 81-91.
    23. Carnero, María Ángeles & Peña, Daniel & Ruiz Ortega, Esther, 2001. "Outliers and conditional autoregressive heteroscedasticity in time series," DES - Working Papers. Statistics and Econometrics. WS ws010704, Universidad Carlos III de Madrid. Departamento de Estadística.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Ahmed, Walid M.A., 2021. "How do Islamic equity markets respond to good and bad volatility of cryptocurrencies? The case of Bitcoin," Pacific-Basin Finance Journal, Elsevier, vol. 70(C).
    2. Catania, Leopoldo & Grassi, Stefano, 2022. "Forecasting cryptocurrency volatility," International Journal of Forecasting, Elsevier, vol. 38(3), pages 878-894.
    3. Cheikh, Nidhaleddine Ben & Zaied, Younes Ben & Chevallier, Julien, 2020. "Asymmetric volatility in cryptocurrency markets: New evidence from smooth transition GARCH models," Finance Research Letters, Elsevier, vol. 35(C).
    4. Sercan Demiralay & Selçuk Bayracı, 2021. "Should stock investors include cryptocurrencies in their portfolios after all? Evidence from a conditional diversification benefits measure," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 26(4), pages 6188-6204, October.
    5. Vahidin Jeleskovic & Mirko Meloni & Zahid Irshad Younas, 2020. "Cryptocurrencies: A Copula Based Approach for Asymmetric Risk Marginal Allocations," MAGKS Papers on Economics 202034, Philipps-Universität Marburg, Faculty of Business Administration and Economics, Department of Economics (Volkswirtschaftliche Abteilung).
    6. José Antonio Núñez-Mora & Mario Iván Contreras-Valdez & Roberto Joaquín Santillán-Salgado, 2023. "Risk Premium of Bitcoin and Ethereum during the COVID-19 and Non-COVID-19 Periods: A High-Frequency Approach," Mathematics, MDPI, vol. 11(20), pages 1-20, October.
    7. Bergsli, Lykke Øverland & Lind, Andrea Falk & Molnár, Peter & Polasik, Michał, 2022. "Forecasting volatility of Bitcoin," Research in International Business and Finance, Elsevier, vol. 59(C).
    8. Cristina Chinazzo & Vahidin Jeleskovic, 2024. "Forecasting Bitcoin Volatility: A Comparative Analysis of Volatility Approaches," Papers 2401.02049, arXiv.org.
    9. Roy Cerqueti & Massimiliano Giacalone & Raffaele Mattera, 2020. "Skewed non-Gaussian GARCH models for cryptocurrencies volatility modelling," Papers 2004.11674, arXiv.org.
    10. Zhiyong Tu & Lan Ju, 2019. "A Normative Dual-value Theory for Bitcoin and other Cryptocurrencies," Papers 1904.05028, arXiv.org.
    11. Paola Stolfi & Mauro Bernardi & Davide Vergni, 2022. "Robust estimation of time-dependent precision matrix with application to the cryptocurrency market," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 8(1), pages 1-25, December.
    12. Charfeddine, Lanouar & Benlagha, Noureddine & Maouchi, Youcef, 2020. "Investigating the dynamic relationship between cryptocurrencies and conventional assets: Implications for financial investors," Economic Modelling, Elsevier, vol. 85(C), pages 198-217.
    13. Fakhfekh, Mohamed & Jeribi, Ahmed, 2020. "Volatility dynamics of crypto-currencies’ returns: Evidence from asymmetric and long memory GARCH models," Research in International Business and Finance, Elsevier, vol. 51(C).
    14. Nikolaos A. Kyriazis, 2021. "A Survey on Volatility Fluctuations in the Decentralized Cryptocurrency Financial Assets," JRFM, MDPI, vol. 14(7), pages 1-46, June.
    15. Zhang, Wei & Li, Yi, 2020. "Is idiosyncratic volatility priced in cryptocurrency markets?," Research in International Business and Finance, Elsevier, vol. 54(C).
    16. D’Amato, Valeria & Levantesi, Susanna & Piscopo, Gabriella, 2022. "Deep learning in predicting cryptocurrency volatility," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 596(C).
    17. Utku Altunoz, 2023. "Analyzing the Volatility Dynamics of Crypto Currency and the Occurrence of Speculative Bubbles: The Examples of Bitcoin, Ethereum, and Ripple," Istanbul Journal of Economics-Istanbul Iktisat Dergisi, Istanbul University, Faculty of Economics, vol. 73(73-1), pages 615-643, June.
    18. Demiralay, Sercan & Golitsis, Petros, 2021. "On the dynamic equicorrelations in cryptocurrency market," The Quarterly Review of Economics and Finance, Elsevier, vol. 80(C), pages 524-533.
    19. Pinar Deniz & Thanasis Stengos, 2020. "Cryptocurrency Returns before and after the Introduction of Bitcoin Futures," JRFM, MDPI, vol. 13(6), pages 1-21, June.
    20. Khanh Hoang & Cuong C. Nguyen & Kongchheng Poch & Thang X. Nguyen, 2020. "Does Bitcoin Hedge Commodity Uncertainty?," JRFM, MDPI, vol. 13(6), pages 1-14, June.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Charles, Amélie & Darné, Olivier, 2017. "Forecasting crude-oil market volatility: Further evidence with jumps," Energy Economics, Elsevier, vol. 67(C), pages 508-519.
    2. Cheikh, Nidhaleddine Ben & Zaied, Younes Ben & Chevallier, Julien, 2020. "Asymmetric volatility in cryptocurrency markets: New evidence from smooth transition GARCH models," Finance Research Letters, Elsevier, vol. 35(C).
    3. Andrea Flori, 2019. "Cryptocurrencies In Finance: Review And Applications," International Journal of Theoretical and Applied Finance (IJTAF), World Scientific Publishing Co. Pte. Ltd., vol. 22(05), pages 1-22, August.
    4. Trucíos, Carlos, 2019. "Forecasting Bitcoin risk measures: A robust approach," International Journal of Forecasting, Elsevier, vol. 35(3), pages 836-847.
    5. Klein, Tony & Pham Thu, Hien & Walther, Thomas, 2018. "Bitcoin is not the New Gold – A comparison of volatility, correlation, and portfolio performance," International Review of Financial Analysis, Elsevier, vol. 59(C), pages 105-116.
    6. Guesmi, Khaled & Saadi, Samir & Abid, Ilyes & Ftiti, Zied, 2019. "Portfolio diversification with virtual currency: Evidence from bitcoin," International Review of Financial Analysis, Elsevier, vol. 63(C), pages 431-437.
    7. T. Takaishi, 2021. "Power-Law Return-Volatility Cross Correlations of Bitcoin," Papers 2102.08187, arXiv.org.
    8. Charles, Amélie & Darné, Olivier, 2014. "Large shocks in the volatility of the Dow Jones Industrial Average index: 1928–2013," Journal of Banking & Finance, Elsevier, vol. 43(C), pages 188-199.
    9. Cristina Chinazzo & Vahidin Jeleskovic, 2024. "Forecasting Bitcoin Volatility: A Comparative Analysis of Volatility Approaches," Papers 2401.02049, arXiv.org.
    10. Mawuli Segnon & Stelios Bekiros, 2019. "Forecasting Volatility in Cryptocurrency Markets," CQE Working Papers 7919, Center for Quantitative Economics (CQE), University of Muenster.
    11. Mawuli Segnon & Stelios Bekiros, 2020. "Forecasting volatility in bitcoin market," Annals of Finance, Springer, vol. 16(3), pages 435-462, September.
    12. Amélie Charles & Olivier Darné, 2019. "The accuracy of asymmetric GARCH model estimation," Post-Print hal-01943883, HAL.
    13. Fakhfekh, Mohamed & Jeribi, Ahmed, 2020. "Volatility dynamics of crypto-currencies’ returns: Evidence from asymmetric and long memory GARCH models," Research in International Business and Finance, Elsevier, vol. 51(C).
    14. Jeffrey Chu & Stephen Chan & Saralees Nadarajah & Joerg Osterrieder, 2017. "GARCH Modelling of Cryptocurrencies," JRFM, MDPI, vol. 10(4), pages 1-15, October.
    15. repec:hal:wpaper:hal-01943883 is not listed on IDEAS
    16. Deniz Erdemlioglu & Sébastien Laurent & Christopher J. Neely, 2013. "Econometric modeling of exchange rate volatility and jumps," Chapters, in: Adrian R. Bell & Chris Brooks & Marcel Prokopczuk (ed.), Handbook of Research Methods and Applications in Empirical Finance, chapter 16, pages 373-427, Edward Elgar Publishing.
    17. Nikolaos A. Kyriazis, 2021. "A Survey on Volatility Fluctuations in the Decentralized Cryptocurrency Financial Assets," JRFM, MDPI, vol. 14(7), pages 1-46, June.
    18. Amélie Charles & Olivier Darné, 2021. "Econometric history of the growth–volatility relationship in the USA: 1919–2017," Cliometrica, Journal of Historical Economics and Econometric History, Association Française de Cliométrie (AFC), vol. 15(2), pages 419-442, May.
    19. Laurent, Sébastien & Lecourt, Christelle & Palm, Franz C., 2016. "Testing for jumps in conditionally Gaussian ARMA–GARCH models, a robust approach," Computational Statistics & Data Analysis, Elsevier, vol. 100(C), pages 383-400.
    20. Panagiotidis, Theodore & Papapanagiotou, Georgios & Stengos, Thanasis, 2022. "On the volatility of cryptocurrencies," Research in International Business and Finance, Elsevier, vol. 62(C).
    21. Charles, Amélie & Darné, Olivier & Pop, Adrian, 2015. "Risk and ethical investment: Empirical evidence from Dow Jones Islamic indexes," Research in International Business and Finance, Elsevier, vol. 35(C), pages 33-56.

    Replication

    This item is a replication of:
  • Katsiampa, Paraskevi, 2017. "Volatility estimation for Bitcoin: A comparison of GARCH models," Economics Letters, Elsevier, vol. 158(C), pages 3-6.

    • More about this item

    Keywords

    Bitcoin; GARCH; Volatility; Jumps;
    All these keywords.

    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C50 - Mathematical and Quantitative Methods - - Econometric Modeling - - - General
    • G10 - Financial Economics - - General Financial Markets - - - General (includes Measurement and Data)

    Lists

    This item is featured on the following reading lists, Wikipedia, or ReplicationWiki pages:
    1. Volatility estimation for Bitcoin: Replication and robustness (International Economics 2019) in ReplicationWiki

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:inteco:v:157:y:2019:i:c:p:23-32. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.sciencedirect.com/science/journal/21107017 .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.